Methods of analyzing and correcting medical imaging data

ABSTRACT

In a method and apparatus for analyzing and correcting medical imaging data of a subject, an image data set is obtained from a scan of the subject at a first time point with respect to a defined time origin. A measurement of a time-dependent variable is then determined for the data set, and an estimated value for the time-dependent variable at an estimate time point is extrapolated from the data set.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention concerns methods and apparatuses for analyzing andcorrecting medical imaging data of a subject.

2. Description of the Prior Art

In the medical imaging field, several nuclear medicine emission imagingschemes are known. For example PET (Positron Emission Tomography) is amethod for imaging a subject in 3D using an ingested radio-activesubstance which is processed in the body, typically resulting in animage indicating one or more biological functions. FDG, for instance, isa glucose analog which is used as the radiopharmaceutical tracer in PETimaging to show a map of glucose metabolism. For cancer for example, FDGis particularly indicated as most tumors are hypermetabolic, which willappear as a high intensity signal in the PET image. For this reason, PETimaging is widely used to detect and stage a wide variety of cancers.The level of glucose activity is usually highly correlated with theaggressiveness and extent of the cancer, and, for example, a reductionin FDG signal between a baseline and a follow-up scan is oftenindicative of a positive response to therapy.

A key criterion used in evaluating suspicious lesions in a PET scan isthe Standardized Uptake Value (SUV). This value is computed from thenumber of counts of emission events recorded per voxel in the imagereconstructed from the event data captured in the PET scan (coincidenceemission events along the line of response). Effectively the SUV'spurpose is to provide a standardized measure of the spatial distributionof radiotracer concentration throughout the imaged portion of the body.

Conventionally, PET scans are acquired using a static protocol,producing a single image volume representing the average counts (pervoxel) detected over a fixed period of time following a given intervalbetween radiotracer injection and image acquisition.

The interval between radiotracer injection and PET acquisition isintended to allow the biological system to reach a steady stateequilibrium, with respect to radiotracer distribution. However, withmany clinical protocols using an interval of 45-60 mins for 18F-FDG,this equilibrium is often not achieved. As such, small differences inthe timing of the acquisition window, post injection (PI) ofradiotracer, can significantly affect the uptake (or SUV) measured for amalignant region.

Despite this fact, variations occur in the PI interval during theclinical imaging process, which may in turn result in themisinterpretation of a difference in uptake (or SUV) between two scans,when in fact it may purely be as a result of different PI intervals.

Currently, efforts are made to maintain consistency in PI interval in aclinical protocol, but variations do still occur as not all factors canbe controlled. Despite these variations, typically no attempts tocorrect for the potential impact of such differences are made;furthermore, the clinician reading the scan is often unaware of suchdifferences.

Some clinical applications that can simultaneously load multiple datavolumes highlight to the user any significant differences in the PIinterval between the two scans have been previously considered (forexample, TrueD from Siemens Healthcare). However, this is only intendedto inform the user that such a different exists and does not attempt tocorrect for the uptake itself.

SUMMARY OF THE INVENTION

An object of the present invention is to address these problems andprovide improvements upon the known devices and methods.

In general terms, one embodiment of a first aspect of the invention canprovide a method of analyzing medical imaging data of a subject,includes obtaining a first image data set from a scan of the subject ata first time point with respect to a defined time origin, determining afirst measurement of a time dependent variable for the first data set,and extrapolating from the first data set a first estimate value for thetime dependent variable at a first estimate time point.

Thus an estimate of a value for the variable can be made at a time pointother than that of the data set in question, so that a different PIinterval can be accounted for.

Preferably, the step of extrapolating includes extrapolating the firstestimate value from the first measurement of the time dependentvariable, and a calculated rate of change of the variable for the firstdata set.

The method can further include obtaining a second image data set from ascan of the subject at a second time point with respect to the definedtime origin, determining a second measurement of a time dependentvariable for the second image data set, and comparing the first estimatevalue for the variable with the second measurement of the variable. Thisallows a comparison of a value at a different time point, for example adifferent PI interval, to be made.

In an embodiment, the first estimate time point is the second timepoint.

In another embodiment, the step of comparing with the second measurementincludes extrapolating from the second data set a second estimate valuefor the time dependent variable at the first estimate time point, andcomparing the first and second estimate values.

In yet another embodiment, the step of comparing includes extrapolatingthe second estimate value at a third time point intermediate the firstand second time points. The method then further includes extrapolatingfirst, second and third estimate values at each of the first, second andthird time points, and comparing the first, second and third estimatevalues.

More preferably, the step of extrapolating includes obtaining from thefirst data set a plurality of measurements of the time dependentvariable within a period of the scan at a given region of the imagingvolume, establishing a sub-period time point within the scan period foreach such measurement, calculating from the measurements and thesub-scan time points a rate of change of the variable for the givenregion, and extrapolating the estimate value for the variable accordingto the calculated rate of change.

The number of measurements of the time dependent variable can be a countof emission events captured by an imaging apparatus.

Preferably, the time dependent variable is uptake of a tracer.

In one embodiment, the first data set is obtained from a first scan ofthe subject at the first time point, and the second data set is obtainedfrom a second scan of the subject at the second time point.

In an alternative embodiment, the first data set and the second data setare obtained from the same scan of the subject. This allows comparisonof different time points within the same scan subject matter, forexample to compare different regions of interest, or different tissuestypes.

More preferably, the method further includes using the first estimatevalue for the time dependent variable at the estimate time point tocorrect a value for the uptake of a tracer for the medical imaging data.

In an embodiment, the method further includes using the first estimatevalue to modify a value for the uptake of a tracer for the medicalimaging data. This allows an uptake value to be modified or corrected onthe basis of the extrapolation, so that, for example, an uptake valueestablished at a first PI interval can be altered to indicate what theuptake value would be at a second PI interval.

In a further embodiment of the invention, a method of analyzing medicalimaging data of a subject captured by a medical imaging apparatusincludes obtaining, by a processor, a first image data set from a scanof the subject by the imaging apparatus at a first time point withrespect to a defined time origin, determining, by a processor, a firstmeasurement of a time dependent variable for the first data set,extrapolating, by a processor, from the first data set a first estimatevalue for the time dependent variable at a first estimate time point,and displaying the first estimate value on a display device.

In another embodiment of the invention, an apparatus for analyzingmedical imaging data of a subject captured by a medical imagingapparatus has a processor that obtains a first image data set from ascan of the subject by the imaging apparatus at a first time point withrespect to a defined time origin, determines a first measurement of atime dependent variable for the first data set, and extrapolates fromthe first data set a first estimate value for the time dependentvariable at a first estimate time point, and a display device thatdisplays the first estimate value.

In another embodiment of the invention, a method of correcting medicalimaging data of a subject captured by a medical imaging apparatus,includes obtaining, by a processor, a first image data set from a scanof the subject by the imaging apparatus at a first time point withrespect to a defined time origin, determining, by a processor, a firstmeasurement of a time dependent variable for the first data set,extrapolating, by a processor, from the first data set a first estimatevalue for the time dependent variable at a first estimate time point,using the first estimate value for the time dependent variable at theestimate time point to correct, by a processor, a value for the uptakeof a tracer for the medical imaging data, and displaying the correctedvalue on a display device.

The invention can also encompasses a computer-readable medium encodedwith computer program codes that when the medium is loaded into or runon a computer, causes the computer to implement a method, according toany of the above described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of the calculation of derivatives oftime activity curves for a PET scan according to an embodiment of theinvention.

FIG. 2 is a diagram illustrating the extrapolation of uptake values fromdifferent scans according to an embodiment of the invention.

FIG. 3 is a diagram illustrating in detail the extrapolation of uptakevalues from different scans according to an embodiment of the invention.

FIG. 4 is a diagram illustrating an apparatus according to an embodimentof the invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

When the following terms are used herein, the accompanying definitionscan be applied:

PET—Positron Emission Tomography

ROI—Region of Interest

VOI—Volume (Region) of Interest

FDG—2-18F-Fluoro-2-deoxy-D-glucose

AUC—Area Under the Curve

SUV—Standardized Uptake Value

TAC—Time-Activity Curve

PI—Post Injection

In an embodiment, the present invention attempts to correct measured ROIintensity/SUV for variations in PI interval using the estimate of theslope of the underlying TAC. This slope can be computed using themethodology described in GB 0818495.4 and another co-pending UKapplication (attorney reference: 2008P19156 GB01), and summarized in thefollowing description.

The method of this embodiment is as follows:

1. Compute a slope estimate (using the methodology described in below)for the region of interest (ROI) corresponding to the physiologicalfeature being studied (e.g., a malignant lesion) from each scan beingcompared.

2. Identify a difference in PI interval based on information in theDICOM header (i.e., 0054,0016: Radiopharmaceutical InformationSequence).

3. Extrapolate (linearly) the measured intensity/SUV from each scan to acommon time point based on the computed slope values and PI intervals(see FIG. 2).

Briefly, the slope estimate methodology essentially computes aderivative image which allows calculation of the change of uptake overtime. With reference to FIG. 1, the acquisition scan is between timepoints 106 and 108, and curves 101 and 103 are the TACs for twodifferent tissue areas. Conventional image processing would usuallymeasure the average of uptake over the scan time period (i.e. a flatline across the scan period 106 to 108). In this methodology, thederivative slopes 102 and 104 for the change of uptake over theacquisition period are derived.

The differing slopes can identify the different tissues types ascancerous and inflamed. If the rate of uptake is increasing, as incancer, the derivative image should have a positive signal. Otherwise,if the rate is decreasing, as for inflammation, the signal would benegative. Differentiation between malignancy and inflammation, or othernon-malignant tissues with high uptake, may therefore be facilitated.

A simple method of calculating this derivative is, rather than averagingan uptake value across the entire scan period, to compute a series ofimages from the same list-mode data by resampling time in smallintervals: for instance, from a 10 minute list-mode scan, 10 1-minuteimages can be reconstructed (or 5 2-minutes images, etc). From these 101-minute divisions, the 10 values of the signal at a specific point canbe fitted with a line, and the slope of the line is therefore anestimate of the derivative.

Returning to the present embodiment of the invention, FIG. 2 is anillustration of an extrapolation method for time-correctingintensities/SUVs. With reference to FIG. 2( a), a first scan result(202) and a second scan (204) result for the ROI in question are shown.An intensity or uptake value (206, 208 respectively) is obtained foreach scan, and a gradient (210, 212) is derived for each, using theslope estimating methodology described immediately above. Using theuptake and slope for each scan's ROI, along with the respective PIinterval (214, 216), the intensities can be extrapolated (FIG. 2( b)) toa common time point 218 (i.e., 50 mins in this example).

This approach assumes the underlying TAC over the period beingextrapolated can be approximated as a linear function. Given staticscans are typically acquired after a 45-60 min PI interval (for18F-FDG), where a second derivative of the TAC is approximately zero,and provided the time interval being extrapolated over is relativelyshort, this assumption should be valid (alternatives to this assumptionare described later).

The extrapolated values from each scan can then be compared with oneanother graphically (e.g., using the multi-time-point facility ofTrueD), or used to scale the image intensity values in the displayedimage volume.

An additional application of this method is to adjust the SUVs from awhole body scan to a common time point. Since the whole body is acquiredfrom multiple, sequential, bed positions, the SUVs measured willrepresent different portions of the underlying TAC and therefore may notbe directly comparable. By projecting each SUV to a common time point,this variation may be corrected.

The following sets out a detailed example for this embodiment of theinvention.

Consider, for example, two PET/CT scans acquired of the same subject,one month apart. In each scan, the same lesion is visible and acomparison of the measured uptake (e.g., SUV) for the lesion betweenscans is to be performed. In the first scan the measured lesion uptakeis 5 SUV, while in the second scan the measured lesion uptake is 4.5SUV. However, the first scan was acquired with a post injection interval(PI) of 60 min (i.e. started at a PI of 60 min), whereas the second scanwas acquired with a PI of 40 min. The difficulty in such a case is indetermining whether the observed difference in SUV is due to aphysiological change in the underlying lesion or the difference in postinjection intervals.

To address this problem, this embodiment of the invention makes use ofthe rate of change of SUV that was calculated for the lesion at eachtime point using the slope estimate methodology described above. In thisexample, the rate of change of SUV for the first scan (302, FIG. 3) is0.02 SUV.min−1 and for the second scan (304, FIG. 3) is 0.04 SUV.min−1.

FIG. 3 is a diagram illustrating integration of rate of change data intothe comparison of measured uptake values acquired from staticacquisitions at different post injection intervals (PI).

The measured uptake in scan 1 is linearly extrapolated (dash-dot-dashline 306) based on the measured rate of change (302), as far as the PIof scan 2 (arrow a) through an intermediate PI (arrow b). The measureduptake in scan 2 is also linearly extrapolated (dashed line 308) as faras the same PIs and the PI of scan 1 (arrow c).

FIG. 3 demonstrates the impact of the measured rates of change of SUV(302, 304) on the comparison of lesion uptake. Although the SUV valuefor scan 2 is lower (4.5 SUV) than for scan 1 (5 SUV), linearextrapolation of the SUV from scan 2 to the PI corresponding to scan 1,based on the measured gradient (308), suggests the SUV at scan 2 may infact have been higher (supposed point 310) if acquired at the same PI asfor scan 1 (arrow c in FIG. 3).

In this example, the linearly extrapolated SUV for a different PI iscomputed simply as follows:SUV*=SUV+[(PI*−PI)·R]  [1]where SUV* is the extrapolated value of the originally measured SUV, PI*is the new PI at which SUV* is to be calculated, and R is the measuredrate of change of uptake.

Extrapolation of the SUVs from both scans to an intermediate PI (arrow bin FIG. 3), suggests the SUV from scan 2 would still be greater at thistime point. These findings may suggest to the clinician that care may berequired with the original results, which appeared to show a drop in SUVbetween the two scans from 5 to 4.5 SUV.

However, as a further example in this case, if the SUV from scan 1 isextrapolated to the PI of scan 2, it would appear than scan 1 would nowhave the higher SUV, at this time point. Since the rate of change ofuptake is not linear over extended periods, care should be taken whenextrapolating over large periods; however, incorporation of rate ofchange information can highlight situations where additional caution isrequired when assessing physiological change in uptake between scans.

Of course, in other cases, it may be that all three such extrapolations“agree” that one SUV should indeed be higher than the other, and indeedin still others, the extrapolations may indicate that an original result(e.g. a drop in SUV between scans) was likely correct.

In embodiments of the invention, the common PI interval time point towhich measured intensities are corrected may be selected as one of thefollowing:

a. Intermediate time point—the time point equidistant from bothacquisitions, or in the case of multiple time points, the mean timepoint (such as point b in FIG. 3).

b. Original time point—one of the original time points (such as point aor point c in FIG. 3); i.e., all measured intensities are extrapolatedto one of the time points acquired (one remains unchanged).

c. Multiple time points—each measured intensity can be extracted to anumber of different time points (either intermediate or original), withthe range of differences in the corrected intensities/SUVs being used asa measure of error or confidence.

In an alternative, instead of an assumed simple linear rate of change ofSUV, the measured intensities may be extrapolated by a non-linearfunction, for example using the relevant portion of a referencetime-activity curve.

The methods described can be used to correct values of uptake of atracer, and/or can be used as a quality control guide for a clinician.For example, in a simple case, one uptake value could be extrapolated tothe time point of a second uptake value for a different scan; if thevalues disagree, a problem with one or other value may be noted.

Following correction of intensity/SUV in a set of scans, the correctedvalues may be shown graphically, e.g., a line graph with the originaland corrected values to aid the clinician in their interpretation of thedata and aid their awareness of the estimated influence of differencesin PI interval in the observed changes in intensity/SUV for a givenregion/lesion.

Alternatively, when one or more regions have been segmented in the scansfor tracking (e.g., liver lesion and liver parenchyma) the computedcorrection factors can be used to scale the displayed images to providea visual indication of changes in intensity between the PI timesconsidered.

As an alternative to a graphical representation, SUV values beingcompared from different scans are presented in tabular form using thisextrapolation method to compute upper and lower bounds on thedifferences. For example, when comparing two scans these bounds can becomputed (i) by projecting both SUV values to an intermediate timepoint, (ii) projecting the first value to the time point of the second,and (iii) projecting the second value to the time point of the first.From these values, the biggest and smallest differences would representthe bounds.

In an alternative application of the methods described above, the (atleast) two different time points may be in the same scan, for differenttissue types. The resultant comparison and/or correction are in suchcase between these tissue types. For example, in a single scan, scanperiods 1 and 2 in FIG. 3 could instead represent sub-periods of thescan addressing different regions of interest, for example the heart at40 mins, and the bladder at 42 mins. Extrapolation between the two givesan indication of whether uptake has altered in the time between thescans of the different regions of interest, and allows correction forthis time lag.

In another alternative, the same method can be used in a case where,rather than the difference between uptake values not being known, theunknown is the PI interval for one of the scans. Thus, where the uptakevalues are known, and a rate of change for each of the uptake values caneither be derived or assumed, a first known PI interval can be used toestimate what the missing or suspect PI interval for a first scan was.

In a further alternative, as opposed to using the slope derived from themethodology described in GB 0818495.4, any temporally-binned data may beused to estimate a slope (e.g., from a dynamic scan, or from a dual timepoint scan).

In addition to PET, these methods may be applied to any imaging modalityfor which information about the body is collected dynamically, butgenerally presented as a static image (e.g., SPECT).

Referring to FIG. 4, the above embodiments of the invention may beconveniently realized as a computer system suitably programmed withinstructions for carrying out the steps of the methods according to theinvention.

For example, a central processing unit 404 is able to receive datarepresentative of medical scans via a port 405 which could be a readerfor portable data storage media (e.g. CD-ROM); a direct link withapparatus such as a medical scanner (not shown) or a connection to anetwork.

Software applications loaded on memory 406 are executed to process theimage data in random access memory 407.

A Man-Machine interface 408 typically includes a keyboard/mouse/screencombination (which allows user input such as initiation of applications)and a screen on which the results of executing the applications aredisplayed.

Although modifications and changes may be suggested by those skilled inthe art, it is the intention of the inventors to embody within thepatent warranted hereon all changes and modifications as reasonably andproperly come within the scope of their contribution to the art.

We claim as our invention:
 1. A method for computer-assisted evaluation of medical images, comprising: providing a computer with multiple data sets, each representing a respective medical image, each of said data sets comprising data representing a time-dependent variable in the respective medical images, said variable changing over time in the respective medical images with respect to a time origin that is common to the respective medical images, and at least two of said data sets being respectively obtained at different points in time with respect to said time origin; in said computer, automatically determining a measurement value of said time-dependent variable in a first of said at least two data sets for a first point in time at which said first of said at least two data sets was obtained; in said computer, automatically determining a measured value of said time-dependent variable in a second of said at least two data sets for a second point in time at which said second of said at least two data sets was obtained, said second point in time being after said first point in time; in said computer, automatically extrapolating an estimated value of said time-dependent variable in said first of said at least two of said data sets from the second measurement of the time dependent variable; and a calculated rate of change of the variable with respect to time in the second data set over a time duration between said first time and said second time, and identifying a difference between said measured value of said time-dependent variable in said first of said at least two of said data sets and said estimated value; and in said computer, generating a visual presentation, at a display screen in communication with said computer, of at least said second of said at least two data sets, with intensity values therein scaled dependent on said difference.
 2. A method according to claim 1, wherein the step of extrapolating comprises: obtaining from the first data set a plurality of measurements of the time dependent variable within a period of the scan at a given region of the imaging volume; establishing a sub-period time point within the scan period for each such measurement; calculating from the measurements and the sub-scan time points a rate of change of the variable with respect to time for the given region; and extrapolating the estimate value for the variable according to the calculated rate of change.
 3. A method according to claim 2, wherein the plurality of measurements of the time dependent variable is a count of emission events captured by an imaging apparatus.
 4. A method according to claim 1 comprising extrapolating the second estimate value at a third point in time intermediate the points in time.
 5. A method according to claim 4, further comprising extrapolating first, second and third estimate values at each of the first, second and third time points, and comparing the first, second and third estimate values.
 6. A method according to claim 1, comprising using uptake of a tracer by a patient from whom the first and the second of said data sets were obtained the time dependent variable is uptake of a tracer.
 7. A method according to claim 1, comprising obtaining the first and the second of said data sets from the same scan of the subject.
 8. A method according to claim 7, further comprising using the first estimate value for the time dependent variable at the estimate time point to correct a value for the uptake of said tracer.
 9. A method according to claim 7, further comprising using the first estimate value to modify a value for the uptake of said tracer for the medical imaging data.
 10. An apparatus for computer-assisted evaluation of medical images, comprising: a computer having an input configured to receive multiple data sets, each representing a respective medical image, each of said data sets comprising data representing a time-dependent variable in the respective medical images, said variable changing over time in the respective medical images with respect to a time origin that is common to the respective medical images, and at least two of said data sets being respectively obtained at different points in time with respect to said time origin; said computer being configured to automatically determine a measurement value of said time-dependent variable in a first of said at least two data sets for a point in time at which said first of said at least two data sets was obtained; said computer being configured to automatically determine a measured value of said time-dependent variable in a second of said at least two data sets for a second point in time at which said second of said at least two data sets was obtained, said second point in time being after said first point in time; said computer being configured to automatically extrapolate an estimated value of said time-dependent variable in said first of said at least two of said data sets from the second measurement of the time dependent variable; and a calculated rate of change of the variable with respect to time in the second data set over a time duration between said first time and said second time, and to identify a difference between said measured value of said time-dependent variable in said first of said at least two of said data sets and said estimated value; a display in communication with said computer; and said computer being configured to generate a visual presentation, at said display, of at least second of said at least two data sets, with intensity values therein scaled dependent on said difference.
 11. A non-transitory, computer-readable data storage medium encoded with programming instructions, said storage medium being loaded into a computer, and said programming instructions causing said computer to: receive multiple data sets, each representing a respective medical image, each of said data sets comprising data representing a time-dependent variable in the respective medical images, said variable changing over time in the respective medical images with respect to a time origin that is common to the respective medical images, and at least two of said data sets being respectively obtained at different points in time with respect to said time origin; determine a measurement value of said time-dependent variable in a first of said at least two data sets for a point in time at which said first of said at least two data sets was obtained; determine a measured value of said time-dependent variable in a second of said at least two data sets for a second point in time at which said second of said at least two data sets was obtained, said second point in time being after said first point in time; extrapolate an estimated value of said time-dependent variable in said first of said at least two of said data sets, said second point in time being after said first point in time, and identify a difference between said measured value of said time-dependent variable in said first of said at least two of said data sets and said estimated value; and generate a visual presentation, at a display screen, of at least said second of said at least two data sets with intensity values therein scaled dependent on said difference. 